{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如何訓練小數據集\n",
    "\n",
    "要用非常少的數據來訓練一個圖像分類的模型是一種在實務上常見的情況，如果您過去曾經進行過影像視覺處理的相關專案，這樣的情況勢必常常遇到。\n",
    "\n",
    "訓練用樣本很\"少\"可以意味著從幾百到幾萬個圖像(視不同的應用與場景)。作為一個示範的案例，我們將集中在將圖像分類為“狗”或“貓”，數據集中包含4000張貓和狗照片（2000隻貓，2000隻狗）。我們將使用2000張圖片進行訓練，1000張用於驗證，最後1000張用於測試。\n",
    "\n",
    "我們將回顧一個解決這種問題的基本策略：從零開始，我們提供了少量數據來訓練一個新的模型。我們將首先在我們的2000個訓練樣本上簡單地訓練一個小型卷積網絡(convnets)來做為未來優化調整的基準，在這過程中沒有任何正規化(regularization)的手法或配置。\n",
    "\n",
    "這樣的模型將使我們的分類準確率達到71%左右。在這個階段，我們的主要問題將是過擬合(overfitting)。然後，我們將介紹*數據擴充(data augmentation)*，這是一種用於減輕電腦視覺演算過度擬合(overfitting)的強大武器。通過利用數據擴充(data augmentation)，我們將改進網絡，並提升準確率達到82%。\n",
    "\n",
    "在另一個文章中，我們將探索另外兩種將深度學習應用到小數據集的基本技術:*使用預訓練的網絡模型來進行特徵提取*（這將使我們達到90%至93%的準確率），*調整一個預先訓練的網絡模型*（這將使我們達到95%的最終準確率）。\n",
    "總而言之，以下三個策略: \n",
    "\n",
    "* 從頭開始訓練一個小型模型\n",
    "* 使用預先訓練的模型進行特徵提取\n",
    "* 微調預先訓練的模型\n",
    "\n",
    "將構成您未來的工具箱，用於解決計算機視覺運算應用到小數據集的問題上。\n",
    "\n",
    "![data augmentation](https://cdn-images-1.medium.com/max/800/1*H0V8WAX9nLEmiD7cd1twow.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform: Windows-10-10.0.15063-SP0\n",
      "Tensorflow version: 1.4.0\n",
      "Keras version: 2.0.9\n"
     ]
    }
   ],
   "source": [
    "# 這個Jupyter Notebook的環境\n",
    "import platform\n",
    "import tensorflow\n",
    "import keras\n",
    "print(\"Platform: {}\".format(platform.platform()))\n",
    "print(\"Tensorflow version: {}\".format(tensorflow.__version__))\n",
    "print(\"Keras version: {}\".format(keras.__version__))\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料集說明\n",
    "\n",
    "我們將使用的數據集(貓與狗的圖片集)沒有被包裝Keras包裝發佈, 因此要自行另外下載。Kaggle.com在2013年底提供了這些數據來作為電腦視覺競賽題目。您可以從以下連結下載原始數據集：`https://www.kaggle.com/c/dogs-vs-cats/data`\n",
    "\n",
    "圖片是中等解析度的彩色JPEG檔。他們看起來像這樣：\n",
    "\n",
    "![cats_vs_dogs_samples](https://s3.amazonaws.com/book.keras.io/img/ch5/cats_vs_dogs_samples.jpg)\n",
    "\n",
    "該原始數據集包含25,000張狗和貓的圖像（每個類別12,500個），大小為543MB（壓縮）。下載和解壓縮後，我們將創建一個包含三個子集的新數據集：一組包含每個類的1000個樣本的訓練集，每組500個樣本的驗證集，最後一個包含每個類的500個樣本的測試集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料準備\n",
    "\n",
    "1. 從[Kaggle](https://www.kaggle.com/c/dogs-vs-cats/data)點擊`Download`下載圖像資料檔`training.zip`。\n",
    "2. 在這個Jupyter Notebook所在的目錄下產生一個新的子目錄\"data\"。\n",
    "3. 把從kaggle下載的資料檔複製到\"data\"的目錄裡頭。\n",
    "4. 將`train.zip`解壓縮\n",
    "  \n",
    "最後你的目錄結構看起來像這樣:\n",
    "```\n",
    "xx-yyy.ipynb\n",
    "data/\n",
    "└── train/\n",
    "    ├── cat.0.jpg\n",
    "    ├── cat.1.jpg\n",
    "    ├── ..\n",
    "    └── dog.12499.jpg\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# 專案的根目錄路徑\n",
    "ROOT_DIR = os.getcwd()\n",
    "\n",
    "# 置放coco圖像資料與標註資料的目錄\n",
    "DATA_PATH = os.path.join(ROOT_DIR, \"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, shutil\n",
    "\n",
    "# 原始數據集的路徑\n",
    "original_dataset_dir = os.path.join(DATA_PATH, \"train\")\n",
    "\n",
    "# 存儲小數據集的目錄\n",
    "base_dir = os.path.join(DATA_PATH, \"cats_and_dogs_small\")\n",
    "if not os.path.exists(base_dir): \n",
    "    os.mkdir(base_dir)\n",
    "\n",
    "# 我們的訓練資料的目錄\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "if not os.path.exists(train_dir): \n",
    "    os.mkdir(train_dir)\n",
    "\n",
    "# 我們的驗證資料的目錄\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "if not os.path.exists(validation_dir): \n",
    "    os.mkdir(validation_dir)\n",
    "\n",
    "# 我們的測試資料的目錄\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "if not os.path.exists(test_dir):\n",
    "    os.mkdir(test_dir)    \n",
    "\n",
    "# 貓的圖片的訓練資料目錄\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "if not os.path.exists(train_cats_dir):\n",
    "    os.mkdir(train_cats_dir)\n",
    "\n",
    "# 狗的圖片的訓練資料目錄\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "if not os.path.exists(train_dogs_dir):\n",
    "    os.mkdir(train_dogs_dir)\n",
    "\n",
    "# 貓的圖片的驗證資料目錄\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "if not os.path.exists(validation_cats_dir):\n",
    "    os.mkdir(validation_cats_dir)\n",
    "\n",
    "# 狗的圖片的驗證資料目錄\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "if not os.path.exists(validation_dogs_dir):\n",
    "    os.mkdir(validation_dogs_dir)\n",
    "\n",
    "# 貓的圖片的測試資料目錄\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "if not os.path.exists(test_cats_dir):\n",
    "    os.mkdir(test_cats_dir)\n",
    "\n",
    "# 狗的圖片的測試資料目錄\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "if not os.path.exists(test_dogs_dir):\n",
    "    os.mkdir(test_dogs_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copy first 1000 cat images to train_cats_dir complete!\n",
      "Copy next 500 cat images to validation_cats_dir complete!\n",
      "Copy next 500 cat images to test_cats_dir complete!\n",
      "Copy first 1000 dog images to train_dogs_dir complete!\n",
      "Copy next 500 dog images to validation_dogs_dir complete!\n",
      "Copy next 500 dog images to test_dogs_dir complete!\n"
     ]
    }
   ],
   "source": [
    "# 複製前1000個貓的圖片到train_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "\n",
    "print('Copy first 1000 cat images to train_cats_dir complete!')\n",
    "\n",
    "# 複製下500個貓的圖片到validation_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "\n",
    "print('Copy next 500 cat images to validation_cats_dir complete!')\n",
    "\n",
    "# 複製下500個貓的圖片到test_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "\n",
    "print('Copy next 500 cat images to test_cats_dir complete!')\n",
    "\n",
    "# 複製前1000個狗的圖片到train_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "\n",
    "print('Copy first 1000 dog images to train_dogs_dir complete!')\n",
    "\n",
    "\n",
    "# 複製下500個狗的圖片到validation_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "\n",
    "print('Copy next 500 dog images to validation_dogs_dir complete!')\n",
    "\n",
    "# C複製下500個狗的圖片到test_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    if not os.path.exists(dst):\n",
    "        shutil.copyfile(src, dst)\n",
    "    \n",
    "print('Copy next 500 dog images to test_dogs_dir complete!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作為一個健康檢查，讓我們計算每次訓練分組中有多少張照片（訓練/驗證/測試）:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training cat images: 1000\n",
      "total training dog images: 1000\n",
      "total validation cat images: 500\n",
      "total validation dog images: 500\n",
      "total test cat images: 500\n",
      "total test dog images: 500\n"
     ]
    }
   ],
   "source": [
    "print('total training cat images:', len(os.listdir(train_cats_dir)))\n",
    "print('total training dog images:', len(os.listdir(train_dogs_dir)))\n",
    "print('total validation cat images:', len(os.listdir(validation_cats_dir)))\n",
    "print('total validation dog images:', len(os.listdir(validation_dogs_dir)))\n",
    "print('total test cat images:', len(os.listdir(test_cats_dir)))\n",
    "print('total test dog images:', len(os.listdir(test_dogs_dir)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以我們確實有2000個訓練圖像，然後是1000個驗證圖像和1000個測試圖像。在每個資料分割(split)中，每個分類都有相同數量的樣本：這是一個平衡的二元分類問題，這意味著分類準確度將成為適當的度量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料預處理 (Data Preprocessing)\n",
    "\n",
    "如現在已經知道的那樣，數據應該被格式化成適當的預處理浮點張量，然後才能餵進我們的神經網絡。目前，我們的數據是在檔案目裡裡的JPEG影像文件，所以進入我們網絡的前處理步驟大概是：\n",
    "\n",
    "* 讀進圖像檔案。\n",
    "* 將JPEG內容解碼為RGB網格的像素。\n",
    "* 將其轉換為浮點張量。\n",
    "* 將像素值（0和255之間）重新縮放到[0,1]間隔（如您所知，神經網絡更喜歡處理小的輸入值）。\n",
    "\n",
    "這可能看起來有點令人生畏，但感謝Keras有一些工具程序可自動處理這些步驟。 Keras有一個圖像處理助手工具的模塊，位於`keras.preprocessing.image`。其中的`ImageDataGenerator`類別，可以快速的自動將磁盤上的圖像文件轉換成張量(tensors)。我們將在這裡使用這個工具。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# 所有的圖像將重新被進行歸一化處理 Rescaled by 1./255\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# 直接從檔案目錄讀取圖像檔資料\n",
    "train_generator = train_datagen.flow_from_directory( \n",
    "        # 這是圖像資料的目錄\n",
    "        train_dir,\n",
    "        # 所有的圖像大小會被轉換成150x150\n",
    "        target_size=(150, 150),\n",
    "        # 每次產生20圖像的批次資料\n",
    "        batch_size=20,\n",
    "        # 由於這是一個二元分類問題, y的lable值也會被轉換成二元的標籤\n",
    "        class_mode='binary')\n",
    "\n",
    "# 直接從檔案目錄讀取圖像檔資料\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=20,\n",
    "        class_mode='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們來看看這些圖像張量產生器(generator)的輸出：它產生150×150 RGB圖像（形狀“（20,150,150,3）”）和二進制標籤（形狀“（20，）”）的批次張量。 20是每個批次中的樣品數（批次大小）。請注意，產生器可以無限制地產生這些批次：因為它只是持續地循環遍歷目標文件夾中存在的圖像。因此，我們需要在某些時候`break`迭代循環。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data batch shape: (20, 150, 150, 3)\n",
      "labels batch shape: (20,)\n"
     ]
    }
   ],
   "source": [
    "for data_batch, labels_batch in train_generator:\n",
    "    print('data batch shape:', data_batch.shape)\n",
    "    print('labels batch shape:', labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讓我們將模型與使用圖像張量產生器的數據進行訓練。我們使用`fit_generator`方法。\n",
    "\n",
    "因為數據是可以無休止地持續生成，所以圖像張量產生器需要知道在一個訓練循環(epoch)要從圖像張量產生器中抽取多少個資料。這是`steps_per_epoch`參數的作用：在從生成器中跑過`steps_per_epoch`批次之後，即在運行`steps_per_epoch`梯度下降步驟之後，訓練過程將轉到下一個循環(epoch)。在我們的情況下，批次是20個樣本，所以它需要100次，直到我們的模型讀進了2000個目標樣本。\n",
    "\n",
    "當使用`fit_generator`時，可以傳遞一個`validation_data`參數，就像`fit`方法一樣。重要的是，這個參數被允許作為數據生成器本身，但它也可以是一個Numpy數組的元組。如果您將生成器傳遞為`validation_data`，那麼這個生成器有望不斷生成一批驗證數據，因此您還應該指定`validation_steps`參數，該參數告訴進程從驗證生成器中抽取多少批次以進行評估。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 網絡模型 (Model)\n",
    "\n",
    "我們的卷積網絡(convnets)將是一組交替的`Conv2D`（具有`relu`激活）和`MaxPooling2D`層。\n",
    "我們從大小150x150（有點任意選擇）的輸入開始，我們最終得到了尺寸為7x7的`Flatten`層之前的特徵圖。\n",
    "\n",
    "注意，特徵圖的深度在網絡中逐漸增加（從32到128），而特徵圖的大小正在減少（從148x148到7x7）。這是一個你將在幾乎所有的卷積網絡(convnets)建構中會看到的模式。\n",
    "\n",
    "由於我們正在處理二元分類問題，所以我們用一個神經元（一個大小為1的`密集層(Dense)`）和一個`sigmoid`激活函數來結束網絡。該神經元將會被用來查看圖像歸屬於那一類或另一類的機率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "from keras import models\n",
    "from keras.utils import plot_model\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們來看看特徵圖的尺寸如何隨著每個連續的圖層而改變："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 打印網絡結構\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在我們的編譯步驟裡，我們使用`RMSprop`優化器。由於我們用一個單一的神經元(`Sigmoid`的激活函數)結束了我們的網絡，我們將使用`二進制交叉熵(binary crossentropy)`作為我們的損失函數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練 (Training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 11s 112ms/step - loss: 0.6955 - acc: 0.5295 - val_loss: 0.6809 - val_acc: 0.5340\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 8s 78ms/step - loss: 0.6572 - acc: 0.6135 - val_loss: 0.6676 - val_acc: 0.5710\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.6214 - acc: 0.6590 - val_loss: 0.6408 - val_acc: 0.6290\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 8s 77ms/step - loss: 0.5882 - acc: 0.6920 - val_loss: 0.6296 - val_acc: 0.6440\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 8s 77ms/step - loss: 0.5517 - acc: 0.7130 - val_loss: 0.7241 - val_acc: 0.5950\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.5108 - acc: 0.7530 - val_loss: 0.5785 - val_acc: 0.7010\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 8s 78ms/step - loss: 0.4899 - acc: 0.7680 - val_loss: 0.5620 - val_acc: 0.7060\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.4575 - acc: 0.7810 - val_loss: 0.5803 - val_acc: 0.6970\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.4193 - acc: 0.8070 - val_loss: 0.5881 - val_acc: 0.7120\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.3869 - acc: 0.8195 - val_loss: 0.5986 - val_acc: 0.7050\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.3620 - acc: 0.8355 - val_loss: 0.6368 - val_acc: 0.7090\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.3434 - acc: 0.8480 - val_loss: 0.6214 - val_acc: 0.6970\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.3165 - acc: 0.8670 - val_loss: 0.6897 - val_acc: 0.7010\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.2878 - acc: 0.8755 - val_loss: 0.6249 - val_acc: 0.7100\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 8s 77ms/step - loss: 0.2650 - acc: 0.8975 - val_loss: 0.6438 - val_acc: 0.7060\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.2362 - acc: 0.9090 - val_loss: 0.7780 - val_acc: 0.6920\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.2098 - acc: 0.9165 - val_loss: 0.8215 - val_acc: 0.6750\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.1862 - acc: 0.9305 - val_loss: 0.7044 - val_acc: 0.7120\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.1669 - acc: 0.9425 - val_loss: 0.7941 - val_acc: 0.6990\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.1522 - acc: 0.9475 - val_loss: 0.8285 - val_acc: 0.6960\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.1254 - acc: 0.9575 - val_loss: 0.8199 - val_acc: 0.7070\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 8s 78ms/step - loss: 0.1117 - acc: 0.9620 - val_loss: 0.9325 - val_acc: 0.7090\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.0907 - acc: 0.9750 - val_loss: 0.8740 - val_acc: 0.7220\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.0806 - acc: 0.9755 - val_loss: 1.0178 - val_acc: 0.6900\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.0602 - acc: 0.9815 - val_loss: 0.9158 - val_acc: 0.7260\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.0591 - acc: 0.9810 - val_loss: 1.1284 - val_acc: 0.7030\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.0511 - acc: 0.9820 - val_loss: 1.1136 - val_acc: 0.7140\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.0335 - acc: 0.9930 - val_loss: 1.4372 - val_acc: 0.6820\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 8s 75ms/step - loss: 0.0409 - acc: 0.9860 - val_loss: 1.2121 - val_acc: 0.6930\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 8s 76ms/step - loss: 0.0271 - acc: 0.9920 - val_loss: 1.3055 - val_acc: 0.7010\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=30,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "訓練完後就把模型保存是個好習慣："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "讓我們使用圖表來秀出在訓練過程中模型對訓練和驗證數據的損失(loss)和準確性(accuracy)數據:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TAb6EuFSS3IXDxCRl8twPEaw+kMA13Zvx0vU98PWWcWCEsAdJ7qLaHU8+x5tr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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef2303c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FfpgGWb/DjT9CXC3mMK8LY2D7XJvUd8yHoDA443Zo3dX23g+m2u+pSbBplp3I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef28ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, label='Training acc')\n",
    "plt.plot(epochs, val_acc, label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, label='Training loss')\n",
    "plt.plot(epochs, val_loss, label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這些圖表顯示了過度擬合(overfitting)的特徵。我們的訓練精確度隨著時間線性增長，直到接近100%，然而我們的驗證精確度卻停在70 ~ 72%。我們的驗證損失在第五個循環(epochs)之後達到最小值，然後停頓，而訓練損失在線性上保持直到達到接近0。\n",
    "\n",
    "因為我們只有相對較少的訓練數據（2000筆），過度擬合(overfitting)將成為我們的首要的關注點。您已經知道了許多可以幫助減輕過度擬合的技術，例如Dropout和權重衰減（L2正規化）。我們現在要引入一個新的，特定於電腦視覺影像，並在使用深度學習模型處理圖像時幾乎普遍使用的技巧：*數據擴充(data augmentation)*。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用數據擴充\n",
    "\n",
    "過度擬合(overfitting)是由於樣本數量太少而導致的，導致我們無法訓練能夠推廣到新數據的模型。\n",
    "\n",
    "給定無限數據，我們的模型將暴露在手頭的數據分佈的每個可能的方面：我們永遠不會過度的。數據增加採用從現有訓練樣本生成更多訓練數據的方法，通過產生可信的圖像的多個隨機變換來“增加”樣本。目標是在訓練的時候，我們的模型永遠不會再看到完全相同的畫面兩次。這有助於模型暴露於數據的更多方面，並更好地推廣。\n",
    "\n",
    "在Keras中，可以通過配置對我們的ImageDataGenerator實例讀取的圖像執行多個隨機變換來完成。讓我們開始一個例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "      rotation_range=40,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      shear_range=0.2,\n",
    "      zoom_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這些只是列出一些可用的選項（更多選項資訊，請參閱Keras文檔）。我們快速看一下這些參數：\n",
    "\n",
    "* `rotation_range`是以度（0-180）為單位的值，它是隨機旋轉圖片的範圍。\n",
    "* `width_shift`和`height_shift`是范圍（佔總寬度或高度的一小部分），用於縱向或橫向隨機轉換圖片。\n",
    "* `shear_range`用於隨機剪切變換。\n",
    "* `zoom_range`用於隨機放大圖片內容。\n",
    "* `horizontal_flip`用於在沒有水平不對稱假設（例如真實世界圖片）的情況下水平地隨機翻轉一半圖像。\n",
    "* `fill_mode`是用於填充新創建的像素的策略，可以在旋轉或寬/高移位後顯示。\n",
    "\n",
    "我們來看看我們的增強後的圖像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lvQ6bpJpKCEXb4PFD/UCHll3fqHSAD7d4GteSdAKMoiVLPq09nJfvvfce3/x+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3MxmfZB41XN4e3Xseoa+IN9VsoW5ZcVqyq6LO2qImjFUXlcHkHLlpumw5OKyQqki3Ph7CQXn1kM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\nDgfbHxMVthSR9xCV2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef129278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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060Dp81sKT+s+/NfAfwxExMdV4MB7H9/QW4RO1I8WRfribY8C7ItKT/bTRmdO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lKKJy1WcsjgPdVxet2B2cK1C8I8bVhjLKWEn6GBIksXS8xa2mPJ1ZeOUlK0CigmNWyl3VO/91YP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef212780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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a57oK2iTLAu9EYh1RkoVj+nGDfn+OT6MI4vjiicZNQdRGNrKRNXlmLIVUosRF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\n601hbWtb2zFbbwprW9vajtn/D4CeSrMS3WsfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef0dd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Kp0Og6vosCcyli9J+UaCFcyFLEkgsVmxe7x3axWufzyumQn4yP6lZauEv0Aon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VlgdmdnJTC7spWdFXvaSOFOlxYopa4AAsjmBvDq0vtOJTD7uc99Nj4rDgN4toCjJ40OjkYGy4nJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ed657470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing import image\n",
    "# 取得訓練資料集中貓的檔案列表\n",
    "fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\n",
    "\n",
    "# 取一個圖像\n",
    "img_path = fnames[3]\n",
    "\n",
    "# 讀圖像並進行大小處理\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "\n",
    "# 轉換成Numpy array並且shape (150, 150, 3)\n",
    "x = image.img_to_array(img)\n",
    "\n",
    "# 重新Reshape成 (1, 150, 150, 3)以便輸入到模型中\n",
    "x = x.reshape((1,) + x.shape)\n",
    "\n",
    "# 透過flow()方法將會隨機產生新的圖像\n",
    "# 它會無限循環，所以我們需要在某個時候“斷開”循環\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    plt.figure(i)\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果我們使用這種數據增強配置來訓練一個新的網絡，我們的網絡將永遠不會看到相同重覆的輸入。然而，它看到的輸入仍然是相互關聯的，因為它們來自少量的原始圖像 - 我們不能產生新的信息，我們只能重新混合現有的信息。因此，這可能不足以完全擺脫過度擬合(overfitting)。為了進一步打擊過度擬合(overfitting)，我們還將在密集連接(densely-connected)的分類器之前添加一個Dropout層："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu',\n",
    "                        input_shape=(150, 150, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(512, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們使用數據擴充(data augmentation)和dropout來訓練我們的網絡："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Epoch 1/50\n",
      "100/100 [==============================] - 21s 208ms/step - loss: 0.6937 - acc: 0.5078 - val_loss: 0.6878 - val_acc: 0.4848\n",
      "Epoch 2/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.6829 - acc: 0.5572 - val_loss: 0.6779 - val_acc: 0.5647\n",
      "Epoch 3/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.6743 - acc: 0.5816 - val_loss: 0.6520 - val_acc: 0.6136\n",
      "Epoch 4/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.6619 - acc: 0.6122 - val_loss: 0.6348 - val_acc: 0.6218\n",
      "Epoch 5/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.6448 - acc: 0.6338 - val_loss: 0.6166 - val_acc: 0.6428\n",
      "Epoch 6/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.6342 - acc: 0.6453 - val_loss: 0.6137 - val_acc: 0.6656\n",
      "Epoch 7/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.6255 - acc: 0.6525 - val_loss: 0.5948 - val_acc: 0.6713\n",
      "Epoch 8/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.6269 - acc: 0.6444 - val_loss: 0.5899 - val_acc: 0.6891\n",
      "Epoch 9/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.6105 - acc: 0.6691 - val_loss: 0.6440 - val_acc: 0.6313\n",
      "Epoch 10/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.5952 - acc: 0.6794 - val_loss: 0.6291 - val_acc: 0.6263\n",
      "Epoch 11/50\n",
      "100/100 [==============================] - 21s 209ms/step - loss: 0.5926 - acc: 0.6850 - val_loss: 0.5518 - val_acc: 0.7049\n",
      "Epoch 12/50\n",
      "100/100 [==============================] - 19s 193ms/step - loss: 0.5830 - acc: 0.6844 - val_loss: 0.5418 - val_acc: 0.7234\n",
      "Epoch 13/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.5839 - acc: 0.6903 - val_loss: 0.5382 - val_acc: 0.7354\n",
      "Epoch 14/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5663 - acc: 0.6944 - val_loss: 0.5891 - val_acc: 0.6662\n",
      "Epoch 15/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5620 - acc: 0.7175 - val_loss: 0.5613 - val_acc: 0.6923\n",
      "Epoch 16/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5458 - acc: 0.7228 - val_loss: 0.4970 - val_acc: 0.7582\n",
      "Epoch 17/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5478 - acc: 0.7106 - val_loss: 0.5104 - val_acc: 0.7335\n",
      "Epoch 18/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5479 - acc: 0.7250 - val_loss: 0.4990 - val_acc: 0.7544\n",
      "Epoch 19/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.5390 - acc: 0.7275 - val_loss: 0.4918 - val_acc: 0.7557\n",
      "Epoch 20/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5391 - acc: 0.7209 - val_loss: 0.4965 - val_acc: 0.7532\n",
      "Epoch 21/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5379 - acc: 0.7262 - val_loss: 0.4888 - val_acc: 0.7640\n",
      "Epoch 22/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5168 - acc: 0.7400 - val_loss: 0.5499 - val_acc: 0.7056\n",
      "Epoch 23/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5250 - acc: 0.7369 - val_loss: 0.4768 - val_acc: 0.7697\n",
      "Epoch 24/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.5088 - acc: 0.7359 - val_loss: 0.4716 - val_acc: 0.7766\n",
      "Epoch 25/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5218 - acc: 0.7359 - val_loss: 0.4922 - val_acc: 0.7544\n",
      "Epoch 26/50\n",
      "100/100 [==============================] - 19s 187ms/step - loss: 0.5143 - acc: 0.7391 - val_loss: 0.4687 - val_acc: 0.7716\n",
      "Epoch 27/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5111 - acc: 0.7494 - val_loss: 0.4637 - val_acc: 0.7671\n",
      "Epoch 28/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4974 - acc: 0.7506 - val_loss: 0.4899 - val_acc: 0.7557\n",
      "Epoch 29/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.5136 - acc: 0.7463 - val_loss: 0.5077 - val_acc: 0.7557\n",
      "Epoch 30/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.5019 - acc: 0.7559 - val_loss: 0.4595 - val_acc: 0.7830\n",
      "Epoch 31/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.4961 - acc: 0.7628 - val_loss: 0.4805 - val_acc: 0.7709\n",
      "Epoch 32/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4925 - acc: 0.7638 - val_loss: 0.4463 - val_acc: 0.7874\n",
      "Epoch 33/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4783 - acc: 0.7700 - val_loss: 0.4667 - val_acc: 0.7824\n",
      "Epoch 34/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4792 - acc: 0.7738 - val_loss: 0.4307 - val_acc: 0.8084\n",
      "Epoch 35/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4774 - acc: 0.7753 - val_loss: 0.4269 - val_acc: 0.8027\n",
      "Epoch 36/50\n",
      "100/100 [==============================] - 19s 191ms/step - loss: 0.4756 - acc: 0.7725 - val_loss: 0.4642 - val_acc: 0.7652\n",
      "Epoch 37/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4796 - acc: 0.7684 - val_loss: 0.4349 - val_acc: 0.7995\n",
      "Epoch 38/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4895 - acc: 0.7665 - val_loss: 0.4588 - val_acc: 0.7836\n",
      "Epoch 39/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4832 - acc: 0.7694 - val_loss: 0.4243 - val_acc: 0.8001\n",
      "Epoch 40/50\n",
      "100/100 [==============================] - 19s 191ms/step - loss: 0.4678 - acc: 0.7772 - val_loss: 0.4442 - val_acc: 0.7773\n",
      "Epoch 41/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.4623 - acc: 0.7797 - val_loss: 0.4565 - val_acc: 0.7874\n",
      "Epoch 42/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4668 - acc: 0.7697 - val_loss: 0.5352 - val_acc: 0.7297\n",
      "Epoch 43/50\n",
      "100/100 [==============================] - 19s 191ms/step - loss: 0.4612 - acc: 0.7906 - val_loss: 0.4236 - val_acc: 0.7951\n",
      "Epoch 44/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4598 - acc: 0.7816 - val_loss: 0.4343 - val_acc: 0.7893\n",
      "Epoch 45/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4553 - acc: 0.7881 - val_loss: 0.4315 - val_acc: 0.7970\n",
      "Epoch 46/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4621 - acc: 0.7734 - val_loss: 0.4303 - val_acc: 0.8027\n",
      "Epoch 47/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4516 - acc: 0.7912 - val_loss: 0.4099 - val_acc: 0.8065\n",
      "Epoch 48/50\n",
      "100/100 [==============================] - 19s 190ms/step - loss: 0.4524 - acc: 0.7822 - val_loss: 0.4088 - val_acc: 0.8115\n",
      "Epoch 49/50\n",
      "100/100 [==============================] - 19s 189ms/step - loss: 0.4508 - acc: 0.7944 - val_loss: 0.4048 - val_acc: 0.8128\n",
      "Epoch 50/50\n",
      "100/100 [==============================] - 19s 188ms/step - loss: 0.4368 - acc: 0.7953 - val_loss: 0.4746 - val_acc: 0.7722\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        # 這是圖像資料的目錄\n",
    "        train_dir,\n",
    "        # 所有的圖像大小會被轉換成150x150\n",
    "        target_size=(150, 150),\n",
    "        batch_size=32,\n",
    "        # 由於這是一個二元分類問題, y的lable值也會被轉換成二元的標籤\n",
    "        class_mode='binary')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        validation_dir,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=32,\n",
    "        class_mode='binary')\n",
    "\n",
    "history = model.fit_generator(\n",
    "      train_generator,\n",
    "      steps_per_epoch=100,\n",
    "      epochs=50,\n",
    "      validation_data=validation_generator,\n",
    "      validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們來保存我們的模型 - 我們將在convnet可視化裡使用它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們再來看一遍我們的結果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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asnDhQiZPnswnn3zC5MmTMRgMtGrVio0bN1p1nquWogJlD9/yGhgKVVrh8a/V\nt1Rqle/QXNm1QaUFSD0J295Qm7WDH1IujL/+G9oPrrpHjpHCIgO7Yy6QlJlPZl4BF/MKycwtIDOv\ngMy8QnzdnRnYwYu+gZ64dhwFf/xHndfVW9nzffvUXqph4KeD53h61VG6ioXcNKALC3sE07OtWym/\n9rzsETitX83LS3/FwNhSJpbPt50mv9DAA4PbwCdnoOVtNZapi09zXr65Fw8vO8iemDSm9W3HyG6t\najxvOQKHwq4PVaBbTZ7KrEBUZCKoD8LDw2VZv/Vjx47RvfuVryWpqToN5neVdAxW3aNW+b2mQtEl\niN4Ajx6t21VzZeRnwetdoNfNKrK1GIMBvp+jsjLe+g0cWwNHf4D7toN3lyqdIjEzj+V7zrJ09xkS\nMkqbEB3tbGjhbI+rox3n0nK5VGTA1kZwk08ir6c9QtSgN2jfdxxOb3eHkc/A8JqbI3IuFbLwpwi+\n2xdHn/YevD0tFD8PF/Od4/bCZ6N5xe1ZPknuzlu3hjIxpC3pOZcY/PIfjOruw7sjbFS07S2LoOdN\nNZYP4L/rjrE1OoXl9wyguVMl5RCrQ24avBKoNuBHlK+XbQ1CiH1SyvDK+umVvqZpYSiC7e/AppdU\nioGp30CPG5RrZOSPyttl5FOVz7P/G8hKVMFOrrW48ov8UblBhpb2tMLGBm76SG1wfn8XFOXD8H9Z\nrfANBsn2k6ks3hXLr5GJFBkkQzu35N8TetCtdXNaONvT3Mmu1KZmXkER+2LT2HEylR0nmnNBunJ0\n6yq+2RbFCzawWfShT34hro7VVyMR8Rk8uPQAp1OymT+yE49c0xk72wqszt7dAMEjwQXsO+7Bw8sO\nYJCSk8nZZF8q4oGRHSHZmAWmZc3MO6Y8Oa47C8bW4b6Wswe0CVZ2/WoqfWvRSl/TdMhKgmUzIG6P\nMp1c/6YyVYCy/3Ydr5T+4IfBoYKNvvNHYM1DKo/M5lcg6Gbof49KOlZTDi4Br07Qrl/5NntnmL4U\nPhutSh0OeazS6aSUrDmcwJsbozmdko2Hiz13DQlkRj9/AirZzHSyt2Vwp5YM7tQSrutKwfIxTDi9\nlR4OkvOZLZm9NgeHXzcyvIs344JaM7q7D27O1q2C8wqK+HZnLK+uj8LdxZ7Fd/U3H7xUFkdX8AzE\nMTWSL+c8wZ2L9vDo8oM42tkypocP3Vq3gMgoVQuglpOn1bkjw+BH1N9UHdNglL72Hrn6udpMheXY\n/7VS+JM/hV63lI8iHfyI8hHf/w0MuNf8HFKqQiHOHjB9uUoUdnCJcrFsN0Ap/+4TLUeeVsSFU6rk\n4OhnLee6d20F9+0AWVSxqyTI6Z6HAAAgAElEQVTKvfC51RHsjU2je5sWvHlrCOOC2lQ7qZd9l2vg\n2A90z0/FEH4nK3oOYt3RBNYfPc/GyEQcbG24pkcrJvf2Y3hXb+zNrNjPXshh8a4zLN9zhrScAkZ2\n9eb1W0Lwcq1CsrNWPSAxkmaOdnw5py93LdrLztOpzB9l9KdPiVY+/dVIoFavWFtqsoY0CKXv5ORE\namoqXl5eWvFfpUgpSU1NxcmpYkVUryT/rfLBFBcDL4t/f1X4esd7KoLUnOI+tAzO7oQb3lMRpu36\nwuh/w4FvYdfHyu7u3Q3u+KXqewMHl6gVamW57h1dK2xOycrn9Q1RLN97Fk8XB16e3Itbwttha1PD\n/52OKiIcacCm6zj6BXrSL9CTf1/fg0Nx6aw+FM/qg/GsPXIer2YOTAxpy81hfvRs24JtJ1L4ekcs\nv/+diACu7eHD7QMDGNSxGv/TPkHq5nwpBxcHFxbd2ZezF3Lp1Mr4vaREq/TOGrM0CKXv5+dHXFwc\nycnWZ9jTXHmcnJzw8/O7cic0FCklaa3SSI6qXBkMfhiWTlObpCG3lm7LTYeN/wa/vqU9ZpzcVObG\n/veq9MWr7oXFt8DsNZUq6FLXcnCpCoRq0da6MWXIKyjimx2xvPP7cXILipg7JJAHR3emRW1tPLZo\nq1bZabEQMKTksI2NoLe/B739VSTrluhkfth/jiW7z7BoewzNney4mFeIVzMH7h/RkRn925dPPFYV\nfHoqM0jy3+AbhqOd7WWFbyhSyeE66TQhlmgQSt/e3r4kElSjAVSI/lcTVSm8616svL/BoDJNmigr\ns3S+Tq3U/3pbPRGY3lA2vQQ5qSq/jbmgHxtb5S1i66gqMa24HWYst87Uc3qzCnYa83zlfU2IT8/l\nj7+T2PR3En+dTCGvwMDIrt48M6EHHb2tvOFUhdELITvZomnJ3taG0d19GN3dh4ycAn45ksCu06mM\n7NqKcb1am41+rTI+xsCoxAjwDSvdlh6rPLFqcRO3sdEglL5GU4qsZPh6klrRnbYydD3jLBTmVr7S\nt7FRq/0f74MTv0Hna9XxhMMqP0r4XdA2tOI5uo1XGSXXPKSCvm78sPJ0xgeXqCeGrtdXeikR8Rn8\ncjiBP/5O4u/zKmivnaczt4a3Y2xQGwZ29Kp0jmpjmje/Etxc7JnR358Z/f1rVwaPAJViIclMmpJk\nY7Usbd6xiFb6moZFbpoqeZd+1hjFuFsV27at5E+5uHSeNVGaQVNUWb9tbymlbzCoaFhnTxj1tHVy\n9pmtvIU2vaA2X8f8p4JrSld+971nWVxBFxkkGyMT+eKv0+w+fQFbG0HfAA+eGt+NUd1a0dHbtens\nd9nYqiRu5nLwFP+eW3a+sjI1ILTS19SMfV+pf0BzLoa1Tf5F+HYKpESpEndZSRCzVXm9VOavnmws\nvO1dccQ0AHYOyka/4SkVDJT8twqRn/SB8tqxlmFPKF/+7e+o+rEDHzDfL+IHKMxTZQPLkJlXwIo9\nZ1m0PYa4tFz8PJx55vru3NKnHW4udRAk1FDw6Ql//6K8qUxvdinR0MwbXHRCQEtopa+pPoYiVRHJ\nuwvcs9X6DVWzcxkgPxOcLRSHuZQDS25VRUFu/UZt1BWXmEuKsELpR4FLS+uVQdhs2PyqWvGfP6xy\nz1TmVVMWIWDcK5CdpG4gTu7Q6Zry/Q58C97doe1l+3RhkYHXf43mmx0xZF8qol+gJ89c34Nre/jU\n3AunMdCqp3LBzUoqnQpCe+5Uilb6muqTfkZFhp4/ovzfrVntF+SpVAJpMaoMX/oZZarJiANDgUqt\n236Iyj8SMATc26uNueWzVAHpmz+Dbka7d8uuIGwhMbLycPuU6Kol4HJ0hb5zYevrykNo/OtVKzNY\njI0t3PQJZKfCTxWU1xzzQslNM6+giPlLDvDbsUQmhbZl3tAOBPm6Vf3cjZmSzdyjl5W+lOrmXkup\nFxorVil9IcRY4G3AFvhMSvlymfY3AaMTLy5AKymlu7GtCCiuenJGSnlDbQiuuQpIOX75/e5PrVP6\na5+AA9+o966tlZL3DYOeN6q0COf2qaLVh5aoPi38lEkl8Yjyje9lUmvY3klFXSZGVHzO6iqD/vcq\n3/uw21SIfHWxd1JePJE/KTNOWWwdSq4rI7eAeV/tZU/sBf4zqSe3DQyo/nkbM6YePMXumdkpkJeu\nV/qVUKnSF0LYAu8D1wJxwB4hxGopZcnWuZTyUZP+DwKm8ei5UspK3B00DZLiTbNeU1XOmOteupzW\nwBxpMSpyNfxOuO6/liNKDUYf7Ni/VEm+xKNw/RtK+ZalVQ9IOFixnNnJShlUNdWuq7cqPehU83rE\nWThx3veGCjdckzLzuP2L3ZxMzuLd6b2ZEFw9f/0mgYsnNG9T2oMnRXvuWIM1K/1+wAkp5SkAIcQy\nYBJgqazTdGBh7YinuapJiVZ28mH/gCMr4MDXMPRxy/23valMJcP+UXEKARsb8OmhfvrNq1gGnyB1\nw8nPshwIVbyJWx1lUM0NwYycAvbEXGDX6VR2n77A0fhMigyS9l4uTO7tx029ffH3upxJMjY1m9s+\n301KVj5f3NGXoZ0ruHlqFD49S3vwlHhoaaVfEdYofV/grMnnOMBsIUchRHsgEPjD5LCTEGIvUAi8\nLKX80cy4u4G7Afz9a9mnV1N3pBxXitS7CwQOh71fqvw1NmYCcNLPwoHFypWxmhGnZvHpoV6T/wY/\nC1lli4uLW+O5U0N+OniOD/88SVTiRaQEB1sbQtu5c9/wjrR2c2LtkQTe+j2aN3+Lpm+AB5PD/OjQ\nshkPLDlAkcHA0nkDCGlX8yeLJoFPTxWnUVSgAuBSosHOWZkENRaxRumbexa1lFlrGvC9lNKknD3+\nUsp4IUQH4A8hxBEp5clSk0n5CfAJqHz6VsikuRpIib68qdp3Lqy4Tdnju5kJMPrrLfU6+JHalaGV\nUeknRlhW+inRqihJbd5syiCl5IM/T/Lahih6tm3Bo9d0oV+gJ6Ht3EslOJs1oD3n0nP58cA5ftgf\nx5M/qO2utm5OfH33oMvpBDSV06qn2uRPPQmtuhk9dyyXSNQorFH6cUA7k89+QLyFvtOAUs7IUsp4\n4+spIcSfKHv/yfJDNQ2KnAuQk3LZZNJ1PDRvC3s+K6/0M+OVe13vmeDervxcNcG9Pdg3q3gzNzlK\nBevUUfBSkUHy3OoIvtkZy42hbXl1SggOdpYVj6+7Mw+M7MT9IzpyOC6DzdHJ3BLuRxu3GuSjaYqY\nevAUK32/KxAv0sCx5pa4B+gshAgUQjigFPvqsp2EEF0BD2CHyTEPIYSj8X1LYDCW9wI0DYliz51i\npW9rB+FzVPHslBOl+/71tkqQZUX+9ypjY6OCw8yF5JfIWkV3zSqQV1DEA4v3883OWO4Z1oE3poZW\nqPBNEUIQ0s6dh0Z31gq/OrTsogq2J0aoOI70s3oT1woq/euUUhYC84ENwDFghZQyQgjxvBDC1P1y\nOrBMlk6q3h3YK4Q4BGxC2fS10m8MmAt3D5sNNvaw9/PLxy6eh32LIGQaeLSvG1l8eqp/fHP5/PMy\n4GJCnSiD9JxL3Pb5LjZEnufZCT14cnx3bHTg1JXDzkHFaiRFqjxMSJ1+wQqs8tOXUq4F1pY59myZ\nz8+ZGbcd6FUD+TRXKynRKpuku8nGe3MfVXrwwGIY9YyqPrX9XbXRVpFXT03x6Qn7v1IpD5q3Zvfp\nC+w/k8a1PXzomFfs0VG7m7jn0nOZ/cVuzqTmaPfK+sSnB5zZWbXcSk0cHZGrqR4px1VZv7KeOn3n\nwtGVcOR7Zeff87lKUezZoe5kMdnMjS9yY+5Xe8jMK+TldX/zoMcuHgdibXyp6XNGcZ3Z5XvPsiHi\nPI62Nnx1Z7+6zWqpqRifnnDkOxURLmzAs3ZLJDZGtNLXVI+UaGht5iHOf6DyqtjzqXrkLsqv21U+\nlGzoGc5H8OjvLhQZJCvuGUhEfAae238gX9ox6otYOrRKZ3CnlhQaDGTlFZKVX8hF42t+oYH2ni50\nbd2crq2b0611Czp4N8Pe1oZz6bl8t/cs3+2N41x6Li2c7JjWtx1zBgcSWEmdWU0d4xOkXiN/Mm7q\nX8WV264StNJviuz5XGWnvGVR9cYX5qvo2qCby7cJAf3mws+PQtIx1aeu7awunuDamugjO9kV25HX\nbwkpKeVHbBYFqZ14tk8wa48ksHT3GVwcbHF1sqO5oz2uTna0buGEna3gdEo2m6OTKTSovQF7W0E7\nDxdOp2YjJQzu5MU/x3blup6tq11nVlPLFD/lXUxQBXA0laKVflPj2M/wy+OAhIlvq8IdVeXCaVWY\n29LmaK+psHGhSoU89IkaiWstmW5dKDobwYTgNtwc5nu5ITkK+zYhzB4UwOxBAZXOk19YxKnkbKLO\nX+TY+UxOJmUzIaQtt/Txo52nS6XjNVeYFm1Vmoy8dL2JayVa6Tclzu2HlXPBqYXyakk9Wb7cnDVU\nVqjC0RWuWagyS7aq+yjYrPxCfknyZLLNDl6c1P1ybpuCXPVEYqkQuhkc7Wzp3qYF3du04EZ8Kx+g\nqV+EUCae2G16E9dKdOhaUyH9rCr47eoNt36rjqVWM0auWOl7dbLcp+9cGPGv6s1fRRb+FMHenNY4\nUoBbjknGkGI3Pq0MGjfFqTi0j75VaKXfFMjLVAVICnJhxneqIAgCUo9XOtQsKcdVfhNLCc6qOl1W\nPl9tj+GJ7w4RGZ9ZpbGrD8Wzcn8coX0GqwOmCbiKc+7oItmNm8DhysTTqnt9S9Ig0Oadxk5RIXx/\np0pINuv7y+YWd3/jSrgapETX2H6anV/Ir5Hn+fFAPNtOpFBkkDjY2bD6UDz/vr47swa0r7Tma1xa\nDk+vOkJvf3emX98bjtgaI3MnX5ZT2FT8RKJp+HSfoFJ/NJUawTVEK/3GjJSwfgGc2AgT3oKOoy63\neXWqntKXUq30zdRztYbY1Gz+92s0GyMTyS0owtfdmbuHdeDGUF+8XB144rtD/PunCP46kcorNweb\nrQNbUGRg7ZEE3vn9OFLC27f2xs7RxVhQxSTgOzlKu/E1FbTCtxqt9Bszuz9V/vKDHlR5cUzx6qSK\nfZctLF0ZF8/DpYslK/2UrHyaO9nhaFe5C2NWfiFzFu0hKTOfyWG+TAr1Jby9R6nUBV/M7svn207z\nyvq/Gf/OVt6ZHkqf9iqnfXrOJZbsPsPX22M5n5lHh5bNeH9m2OW89D49VQ3dYpKjtD1foymDVvqN\nlcx42Pis8l2+5vny7V6d4FJWSeoCqzFu4p618eN/yw6w5nACvdu5881d/XF2sKz4pZT8a+VhYlKy\nWTx3gMUoVhsbwbxhHegb6MlDSw8w9eOdPDCyExey81m57xy5BUUM6dSS/07uxfAu3qVz3bTqCRGr\nVEEVOyf1JNNljPXXptE0AbTSb6z88YLypR//qvn84i2Ndu7UE1VS+meOH8IfmPJ9ChcdBBOD27D6\nUDz3L97HJ7eHY29r3jdg0fYYfjmcwL/GdrMqbUFoO3d+fmgIT/1whHd+P46DnQ03hrblziGBdGvd\nwvygYi+OpGOqrq6hQG/iajRl0Eq/MZJwCA4uUWYdjwDzfYo3N1OOQ8CQSqfcfjKFd34/znVntjHV\nzonpo/sxe1AgHs0c6N/Biyd/OMI/vjvEG1NDy2Wa3Bd7gRd/OcY13X24d7j1OXhaONnz7vTezB3a\nAT8PZ1q6OlY8oDg6MykCmhnLDWrzjkZTCq30GxtSwoan1Uq3opw3LfxUlkwrNnMj4jOY+dkufJo7\ncW2rDJwcuvHItZeV6fR+/lzIvsRrG6Jwd3Fg4cQeJZ43KVn5PLD4AG3dnfnf1JBKPXLKIoQg1Nry\ngSUFVSIvP73oKE2NphTaT7+xEb1e5dUZ+RQ4V6AsbWyUt4sVAVqL/orByc6WDY8Mw68wDttW5VfP\n94/oyNwhgSzaHsO7f6gbSZFB8vCyA6TlXOLDWWG4OZf3xKlViguqJ0WqTdzmbaqXZkKjacTolX5j\noqgAfn0GvDpDnzsq7+/VEZL+rrBLWvYlfjoUz5Q+frjZ5kNmnNnVsxCCp8Z3Jy2ngDc2RuPRzIGk\nzDz+OpHKqzcH07PtFVK+rXrAsTVqk1pHaGo05dBKvzGx90tlrpm+DGytWFV7dYKodSqAy9b8n8Ky\nPWe5VGhg9sCAy6YgC8rUxkbwys29yMgt4NmfjiIlTA33Y2rfWq6LWxHFBVXyMyH8rit3Xo2mgaDN\nO42F3HT4878QOAy6jLVujFdnMBRCeqzZ5iKD5NudsQzo4EnX1s3L18U1g52tDe/N6M2QTi3p7e/O\n85OCqnolNaN4M9dQCN56pa/RlEWv9BsLW1+H3DQY86L1wVZeJm6bXuUrDv12LJFz6bn8e4Ixp0lx\nWoNKqmA52dvy9Z39kJIrXzPWWFAF0O6aGo0Z9Eq/MXDhNOz6GEJnQptg68eZKn0zfL0jhrZuTlzT\n3UcdSIlWLqB2lbhOomz89VIk3MVTbeCCdtfUaMyglX5j4Pf/Axs7VYy8Krh4quyEZpT+8cSL/HUi\nlZkD2mNXHHCVcrxhbI626qGuq9hXX6PRlGCV0hdCjBVCRAkhTgghFphpf1MIcdD4Ey2ESDdpmy2E\nOG78mV2bwmuAwkuqPmifO6BFm6qNFcJi4rWvd8TiYGfDtOJNWEOR6tcQ/N6H/wuu/59OwqXRmKFS\nm74QwhZ4H7gWiAP2CCFWSylL0hlKKR816f8g0Nv43hNYCIQDEthnHJtWq1fRlMk4C9JwuUB0VfHq\npPz6TcjMK2Dl/jgmBrfFqzgKNv2MKnLeEFb6/v2B/vUthUZzVWLNSr8fcEJKeUpKeQlYBkyqoP90\nYKnx/XXARinlBaOi3whY6VqisYq0GPVqKd1CZXh1gsxzcCm75ND3e+PIuVTE7EHtL/ezwnNHo9Fc\n/Vij9H0Bkxp0xBmPlUMI0R4IBP6oylghxN1CiL1CiL3JycnWyK0pptjd0qN9xf0sUZx47cIpAAwG\nyTc7Y+nt706wn0lEb0ldXK30NZqGjDVK35xhVFroOw34XkpZVJWxUspPpJThUspwb2+9+VYl0mLA\n1uGyx0pVMU28Bmw5nszplGwVjGVKSjS4eKnNX41G02CxRunHAaYhlX5AvIW+07hs2qnqWE11SIsF\nt3ZgU3kRE7MU+9wbc/B8vSOWlq6OjO9V5ibSUDx3NBpNhVij9PcAnYUQgUIIB5RiX122kxCiK+AB\n7DA5vAEYI4TwEEJ4AGOMxzS1RVpM9e35AA7NoIUv+YnRrNhzlk1RSczo1w4HuzJ/GrVQF1ej0dQ/\nlXrvSCkLhRDzUcraFvhCShkhhHge2CulLL4BTAeWSSmlydgLQoj/oG4cAM9LKS/U7iU0cdJjwbdP\ntYaeSc3h18jz9M/zpvDofv65/zDtvVyYNbDM/kB2CuSk6JW+RtMIsCoNg5RyLbC2zLFny3x+zsLY\nL4AvqimfpiLyMlTqhSps4uYVFLF09xmW7j5DdGIWAO+1aMM19tv4ae4gevm5l4+kPfqDeg0YWluS\nazSaekLn3qkvtrwO5/bB9KWV97VEWrHnTkClXQuKDKzcF8c7vx8nPiOPMH93nrm+O2N6tMY/OhY2\nrCPEywBlFb6UsG8RtAmFtqHVl1Wj0VwVaKVfH6QcVxkxDUXKP96hWfXmKfbRd7e80jcYJGsOx/Pm\nxmhiUnMIbefOa7eEMKij1+UqViU5eI5DszL1a8/tU+UHJ7xZPRk1Gs1VhVb69cGGp1TqX1BFTPyq\nZ5Mv9tF/eWce8fkHcLSzwdHeBkc7W5zsbbC3tWHdkfNEJV6kW+vmfHZ7OKO7typfsrA4w2bqCfAf\nULpt3yJVgjBoSvVk1Gg0VxVa6V9pon+F479C37mw5zNIPFotpZ+Ymcfp/fvoLpvxxf40fN2dyS8o\nIr/QQH6hgbyCIgoNkg7ezXh3em+u79XGctZL9/YqYVvZHDx5mXB0JQTdDE4tqnGxGo3makMr/StJ\n4SXY8KQyp1z3EhxcCokRVZoiNSufD/88yTc7Y/nE5hRZzXzZcu9IWrs5lT9dkQFbG1F5MXJbO/AI\nLK/0j34PBTnQZ06VZNRoNFcvWulfSXZ/rBTrjO9UTvriIt5WcKnQwHt/HOfzbafJLSjipt5+DErI\nwr51TzCj8IHLKZGtwatT+SLp+75Sidx8w6yfR6PRXNXofPpXiqwk2PwqdB4DXcaoYz49lXlHWspq\nYRyaX8hdX+3hnT9OMKJrK359dBj/m9IL+8yzNQvMMsWro1L6BoP6HH8QEg6qlM06RbFG02jQSv9K\n8fvzylRy3UuXj7XqqfzsLyZYHJZ8MZ9pn+xg+8lUXpsSzPszw+jUqjlknYeiS9VPtFaWlp1V6uTM\nOPV5/1dg5wy9bqmd+TUazVWBNu9cCeIPwIFvYeADpVMZFNdzTYyAFm3LDYtJyeb2L3aTdDGPT2/v\nw6huPpcba5pSuSymidecPeHwd9DzJnB2r3icRqNpUGilX9dICev+Bc1awvB/lm7z6aFeEyOg87Wl\nmo7EZTBn0W6KDJKl8wbQ29+j9NjiwCz3gNqRs8RX/yRkxsOli9BHFzrTaBobWunXNUe+h7O74IZ3\nwcmtdJuzB7TwLefBsyU6mXu/3YeHiwNf39WPjt6u5edNiwEEuLcr31YdXH3AwVVtNJ/bB97doJ2u\nPqXRNDa00q8rci7Altdg96cqhUHoTPP9fHqWUvq7TqVy56I9dPZpzqI5ffFpYd4zh/RYZRKyc6wd\neYVQm7nR61RpxOv+qzdwNZpGiN7IrW0K82H7u/BOKOz6CEKnw8zvLOe79+mp0hYXXgLg4y2n8Gjm\nwPJ7BlhW+FDzlMrm8OqkFL6tI4RMq925NRrNVYFe6dcWUkLED/Dbc0pxdroWrn3+st3eEj5BYCiA\n1OPEOQSyKSqJ+SM70cLJvuJxabHQcWStiQ+Al3GTuccNukKWRtNI0Uq/NigqgG9ugpitSonftgo6\njrJubKvLm7lLE2wRwLR+/hWPKciDi/EVJlqrFq26q1cdgavRNFq00q8NIn9SCv/a/yi3zKqULmzZ\nGWzsKTp/lOV7PBnVrRW+7s4Vj8kw1pqvbfNO94lw95/QtnftzqvRaK4atE2/pkgJ299RppGB86te\nq9bWHry7kXpyPylZ+czsb8XqvcRHv5ZX+ja2WuFrNI0crfRrSsw2SDhkXOFX8+v06YFt8jH8PJwZ\n1sW78v61HZil0WiaDFrp15Tt74JLyxp5u6Q064yXIYU7erthayn9sSlpMWDnpHzrNRqNpgpopV8T\nkqPg+AboNw/sK7HDV8CGZFWtaopfpnUD0mLUJq72o9doNFVEK/2asOM9teLuO7faU+QVFPHFCXXD\ncL8Ybd2g9Njat+drNJomgVVKXwgxVggRJYQ4IYRYYKHPVCFEpBAiQgixxOR4kRDioPFndW0JXu9k\nJcGh5RA6Q+XVscBPB8+xKSrJYvvPhxM4mdecAkcPlWa5MqRUPvranq/RaKpBpS6bQghb4H3gWiAO\n2COEWC2ljDTp0xl4EhgspUwTQrQymSJXShlay3LXP7s/VamNBzxgtrmwyMDzP0fy9Q6VGO3+ER15\nfEzXcjb7b3fG0tHbFTuvIEi0oqBKbhrkZ9a+j75Go2kSWLPS7weckFKeklJeApYBk8r0mQe8L6VM\nA5BSWl7aNgYu5aj6tl3HQ8tO5Zoz8wqYs2gPX++IZd7QQKb38+eDP08yZ9Ee0nMulfQ7ei6Dg2fT\nmdm/PcInSFXRKi5iYgntuaPRaGqANUrfFzhr8jnOeMyULkAXIcRfQoidQoixJm1OQoi9xuM3mjuB\nEOJuY5+9ycnJVbqAeuHQEsi9AIPml2s6k5rDzR9sZ8fJVF6e3Iunr+/Bfyf34uXJvdh5MpWJ720j\nMl5t2C7edQYnextuDvNT6RoKciDtdMXnTjemVNY2fY1GUw2sUfrmXETK1vezAzoDI4DpwGdCiOLq\nG/5SynBgBvCWEKJjucmk/ERKGS6lDPf2tsJPvT4xFMGO98G3D/gPLNW0J+YCN37wF0kX8/n6rn6l\n0ilM6+fP8nsGUFAomfzhXyzeFctPB88xMbgtbi72lwuqVFYzt3ilr807Go2mGlij9OMA06TtfkC8\nmT4/SSkLpJSngSjUTQApZbzx9RTwJ9CwQz6j1sGFUzDowVIuk6sOxDHz0124Oduz6v5BDOpYfnO3\nt78Hax4cQrCfO0+vOkrOpSJmDjAqb+/ugCiXW78cabGqspVTi1q8KI1G01SwRunvAToLIQKFEA7A\nNKCsF86PwEgAIURLlLnnlBDCQwjhaHJ8MGDFbuVVzPZ3wd0fuk0sOfTtzlgeXX6IsPburLp/EB3M\nFT0x4t3ckcVz+3Pv8I5M6eNHiJ+xsIqDC3h2qNyDpy5SKms0miZDpd47UspCIcR8YANgC3whpYwQ\nQjwP7JVSrja2jRFCRAJFwD+klKlCiEHAx0IIA+oG87Kp10+D4+xuOLsTxr4CtuqrW7LrDM/8eJRR\n3Vrx4awwHO0qz71jb2vDgnHdyjf49Kxc6afHqqIsGo1GUw2syrIppVwLrC1z7FmT9xJ4zPhj2mc7\n0KvmYl4lbH4VXLwg7DYAlu85w1OrjjCiq7fVCr9CfILg2Bq4lA0Ozcq3G4og/Sz0KOs8pdFoNNah\nI3Kt5dw+OLFR2fIdmvHd3rMs+OEIw7p489GsPjVX+GAsuCIh6W/z7ZnxquCKNu9oNJpqopW+tWx+\nTRUy7zuXlfvi+OfKwwzp1JJPbuuDk30tKHy47MFjycSjPXc0Gk0N0UrfGhIOqYLhAx5gVWQGT3x/\niEEdvfj09vDaU/gA7gFg38yy22aJj35A7Z1To9E0KbTSt4bNr4KjG4d8p/L4ikMMCPTis9v71q7C\nB5WP36eHZbfNtBgQNuDmV7vn1Wg0TQat9Cvj/FH4+2cYcB9vbk3E3cWBT2eH4+xQywq/mFY9lHlH\nlo1/Q/nou/mpalsajdpIV7gAAA/zSURBVEZTDbTSr4wtr4JDcyL8Z/BnVDJ3DQnE1bEOSwu3DVVJ\n1ZbPUmYlU4rz6Gs0Gk010Uq/IhIjVdHz/vfw1l/JuDnbc/vAOla6obNg+AJVaP3jYbBkmvIcAh2Y\npdFoaoxW+hWx9XWwb0Z0h9vZGJnInMEBNHeqY9OKnQOMfBIeOQKjnlHBYJ+Ogm8mQ3aSTrSm0Whq\nhFb6lkiOhqM/QL95vL09FVdHO+YMCrxy53dyg2H/UMr/mucg4aA67lU+lbNGo9FYSx0apxs4W18H\ne2dOd76DtX9Ecv+Ijiob5pXGsTkMeRT63Q2nNkPnMVdeBo1G02jQK31zpByHI99B+J28vTMdZ3tb\n7hrSoX5lcmgG3caX5PzRaDSa6qCVflnO7oZFE8C+GWe6zWX1oXhmDWiPZzOH+pZMo9FoaoxW+qbs\nWwRfjgd7J7hrA+/szsTe1oa5Q6+gLV+j0WjqEK30AQovwZpHYM3DEDgU5m3irH0gqw6cY3o/f1o1\nd6pvCTUajaZW0Abii+dhxe1wdhcMfgRGPws2tnyw/gi2QnDv8HLVHTUajabB0rSVfsIhWDwV8jNh\nypcQNBmA+PRcvt93llv7tqO1m17lazSaxkPTVvq//R9IA9y1EVoHlRxesusMRQapV/kajabR0XRt\n+lLCub3QdVwphS+lZO2RBAZ29MLPw6UeBdRoNJrap+kq/QunIC8DfPuUOhyVeJFTKdmMC2pTT4Jp\nNBpN3dF0lX5xErMySn/tkfMIAdf1bF0PQmk0Gk3d0rSVvr0LeHcrdXjdkQT6BXji3dyxngTTaDSa\nusMqpS+EGCuEiBJCnBBCLLDQZ6oQIlIIESGEWGJyfLYQ4rjxZ3ZtCV5jzu2DNqGl0hocT7zI8aQs\nrg/Wph2NRtM4qdR7RwhhC7wPXAvEAXuEEKullJEmfToDTwKDpZRpQohWxuOewEIgHJDAPuPYtNq/\nlCpQeAkSDkO/eaUOa9OORqNp7Fiz0u8HnJBSnpJSXgKWAZPK9JkHvF+szKWUScbj1wEbpZQXjG0b\ngbG1I3oNSIqAovxy9vx1RxMIb++BTwvtm6/RaBon1ih9X+Csyec44zFTugBdhBB/CSF2CiHGVmEs\nQoi7hRB7hRB7k5OTrZe+upjZxD2ZnMXf5y8yvpc27Wg0msaLNUpfmDlWtmq3HdAZGAFMBz4TQrhb\nORYp5SdSynApZbi3t7cVItWQc/vBpSW4+5ccWnckAYCxQdq0o9FoGi/WKP04oJ3JZz8g3kyfn6SU\nBVLK00AU6iZgzdgrz7l9apUvLt+T1h45T5i/O23cnOtRMI1Go6lbrFH6e4DOQohAIYQDMA1YXabP\n/7d3rzFSnfcdx78/lsXgtWF3zZLA7nKxTVrigsEgHNVVm1gNxa4LruxKLqlkS40sVUVJ1Utk+sJR\niSK1b9q88Ys6DapflNipmzibhNYlaaJGqkq5eDEYQkyIl70Au7AXLsbs7d8Xc7CH8eAdm9k9yzm/\nj7SaeZ45Z+b/iOE3j55z5swrwGcAJM2nsNxzAngV2CCpQVIDsCHpS8+VC9B37JqlnbfOXuLIqfNe\n2jGzzJvw7J2IGJW0lUJY1wA7IuINSduBfRHRxnvhfgQYA/4qIs4BSPoKhQ8OgO0R0T8ZA6lYTzsQ\n14T+vx8+DcBDDn0zy7iKLrgWEbuAXSV9zxbdD+DPk7/SfXcAO26szCp69yDufe927Tp0intb62mu\n99KOmWVb/r6R270fGpbBrY0AdPa/zaHuIR72AVwzy4Echv6BkqWdwlk7Xs83szzIV+hfOA3nu64J\n/R8cOs3K5nm0NvoyymaWffkK/e4Dhdsk9LsG3uZg5yAPrfTSjpnlQ85Cfz+oBhauAuA/krN2Hva1\n880sJ/IX+h+7B2rnEBF8+0A39yyay9L5dWlXZmY2JfIT+uPj0PPeQdyDXUMcOXWeJ9YvnmBHM7Ps\nyE/ol/w84s49Hdw6q4ZHVy9KuTAzs6mTn9AvurLm0OUR2g72sHn1Im6fXZtuXWZmUyhfoV9bB02/\nwiuvdfPOyDhb1i9JuyozsymVr9BftIbQDHbuOcmqlnmsbJmXdlVmZlMqH6E/OgynX4fm+9jfMcCx\nMxfY4gO4ZpZD+Qj9M4dhbBia17Jzz0luu2Umv3evD+CaWf7kI/STg7hDd6zi+4dO8ftrmqm7paIL\njJqZZUpOQv8A1C3gX38eDI+Os+V+L+2YWT7lI/R7DhDNa9i5t5P7FtezYuHctCsyM0tF9kN/fBz6\nT3Cqdgkn+i6x5X6fpmlm+ZX90L94GsaG+WlfHXNnz+SRVb64mpnlV/ZDf6ADgFd7ZvHY2hZm19ak\nXJCZWXqyH/qDhdB/a6yJz/kArpnlXPZDP5npL1z8Ce5ecHvKxZiZpaui0Je0UdIxScclPVPm8ack\n9UlqT/4+X/TYWFF/WzWLr8SVsyc4HQ08sKJ5ql/azGzamfAbSpJqgOeAzwJdwF5JbRFxpGTTlyJi\na5mnuBwRq2+81I/mcu8JOqOJ1a31aZVgZjZtVDLTXw8cj4gTETEMvAhsntyyqmfG0Em6YgErm31x\nNTOzSkK/GegsanclfaUek/S6pJcltRb1z5a0T9L/Snr0Ror90MZGuO1KL5dubfZ1883MqCz0VaYv\nStrfA5ZGxCrgh8ALRY8tjoh1wBbga5Luet8LSE8nHwz7+vr6Kix9YjHYyQzGmTV/WdWe08zsZlZJ\n6HcBxTP3FqCneIOIOBcRV5Lm14G1RY/1JLcngJ8Aa0pfICKej4h1EbGuqanpQw3gg/Se/DkADc3L\nq/acZmY3s0pCfy+wXNIySbOAJ4BrzsKRVPw1103A0aS/QdItyf35wANA6QHgSXOq42cALLlrxVS9\npJnZtDbh2TsRMSppK/AqUAPsiIg3JG0H9kVEG/AFSZuAUaAfeCrZfQXwj5LGKXzA/G2Zs34mzYXT\nv2Akarjzzk9M1UuamU1rFV1UPiJ2AbtK+p4tur8N2FZmv/8BVt5gjR/dQAfnZjbx8VofxDUzgwx/\nI3d4dJzb3+nmcl1L2qWYmU0bmQ39Y6cv0EwvNY1L0y7FzGzayGzoH/5lN006z7yFd6ddipnZtJHZ\n0D/5y2MAzHXom5m9K7Ohf/7UcQDUsDTdQszMppFMhv7Q5RFmnk+uHNHgn0c0M7sqk6F/qGuIVvUy\nVjMH6qr3DV8zs5tdJkO/vXOAVvVB/WJQuUsHmZnlU0ZDf5C7a8/6dE0zsxKZC/2IoP3kIM30ej3f\nzKxE5kK/e/Ayo5fOMXv8bah36JuZFctc6B/sHCqs54Nn+mZmJTIX+u2dAyybebbQ8EzfzOwaGQz9\nQdbOHSo0PNM3M7tGpkJ/dGycQ91D3DNnEGbXw2z/GLqZWbFMhf6xMxd4Z2Sc1hl9nuWbmZWRqdA/\n2FlY1mkc7vF6vplZGZkK/fbOARrn1DDzQpdn+mZmZWQs9Af5zUXjaGzYM30zszIyE/oXr4zyZu9F\nfr3xYqHDl1Q2M3ufzIT+yOg4f/Jbd3F/QxL6numbmb1PZkK/oW4WX9r4qyyZkXwbt35xugWZmU1D\nFYW+pI2Sjkk6LumZMo8/JalPUnvy9/mix56U9Gby92Q1iy9rsANu+zjUzp70lzIzu9nMnGgDSTXA\nc8BngS5gr6S2iDhSsulLEbG1ZN9G4MvAOiCA/cm+A1WpvpyBDp+5Y2Z2HZXM9NcDxyPiREQMAy8C\nmyt8/t8BdkdEfxL0u4GNH63UCg12eD3fzOw6Kgn9ZqCzqN2V9JV6TNLrkl6W1Pph9pX0tKR9kvb1\n9fVVWHoZYyNwvtszfTOz66gk9Mv93mCUtL8HLI2IVcAPgRc+xL5ExPMRsS4i1jU13cBv2g51Qox7\npm9mdh2VhH4X0FrUbgF6ijeIiHMRcSVpfh1YW+m+VTXQUbj1TN/MrKxKQn8vsFzSMkmzgCeAtuIN\nJC0sam4Cjib3XwU2SGqQ1ABsSPomx2AS+p7pm5mVNeHZOxExKmkrhbCuAXZExBuStgP7IqIN+IKk\nTcAo0A88lezbL+krFD44ALZHRP8kjKNgoANUA3PLHXIwM7MJQx8gInYBu0r6ni26vw3Ydp19dwA7\nbqDGyg12wLwWqKloWGZmuZOZb+QCPkffzGwC2Qp9n6NvZvaBshP6w5fgkn8xy8zsg2Qn9Ecuw689\nDovuS7sSM7NpKztHPOvmw+PfSLsKM7NpLTszfTMzm5BD38wsRxz6ZmY54tA3M8sRh76ZWY449M3M\ncsShb2aWIw59M7McUcT7fsgqVZL6gI4beIr5wNkqlXMz8bjzxePOl0rGvSQiJvzpwWkX+jdK0r6I\nWJd2HVPN484XjztfqjluL++YmeWIQ9/MLEeyGPrPp11ASjzufPG486Vq487cmr6ZmV1fFmf6ZmZ2\nHQ59M7McyUzoS9oo6Zik45KeSbueySRph6ReSYeL+hol7Zb0ZnLbkGaN1SapVdKPJR2V9IakLyb9\nWR/3bEn/J+lgMu6/SfqXSdqTjPslSbPSrnUySKqR9Jqk7yftvIz7LUmHJLVL2pf0VeW9nonQl1QD\nPAc8BHwS+ENJn0y3qkn1z8DGkr5ngB9FxHLgR0k7S0aBv4iIFcCngD9N/o2zPu4rwIMRcS+wGtgo\n6VPA3wH/kIx7APjjFGucTF8Ejha18zJugM9ExOqi8/Or8l7PROgD64HjEXEiIoaBF4HNKdc0aSLi\nv4H+ku7NwAvJ/ReAR6e0qEkWEaci4kBy/wKFIGgm++OOiLiYNGuTvwAeBF5O+jM3bgBJLcDvAv+U\ntEUOxv0BqvJez0roNwOdRe2upC9PPhYRp6AQkMCClOuZNJKWAmuAPeRg3MkSRzvQC+wGfgEMRsRo\nsklW3+9fA74EjCftO8jHuKHwwf6fkvZLejrpq8p7PSs/jK4yfT4XNYMk3Qb8G/BnEXG+MPnLtogY\nA1ZLqge+A6wot9nUVjW5JD0C9EbEfkmfvtpdZtNMjbvIAxHRI2kBsFvSz6r1xFmZ6XcBrUXtFqAn\npVrSckbSQoDktjfleqpOUi2FwP+XiPh20p35cV8VEYPATygc06iXdHXSlsX3+wPAJklvUViufZDC\nzD/r4wYgInqS214KH/TrqdJ7PSuhvxdYnhzZnwU8AbSlXNNUawOeTO4/CXw3xVqqLlnP/QZwNCL+\nvuihrI+7KZnhI2kO8NsUjmf8GHg82Sxz446IbRHREhFLKfx//q+I+BwZHzeApDpJt1+9D2wADlOl\n93pmvpEr6WEKM4EaYEdEfDXlkiaNpG8Cn6ZwudUzwJeBV4BvAYuBk8AfRETpwd6blqTfAH4KHOK9\nNd6/prCun+Vxr6Jw0K6GwiTtWxGxXdKdFGbAjcBrwB9FxJX0Kp08yfLOX0bEI3kYdzLG7yTNmcDO\niPiqpDuowns9M6FvZmYTy8ryjpmZVcChb2aWIw59M7McceibmeWIQ9/MLEcc+mZmOeLQNzPLkf8H\nvsZv5dOudTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17088feeeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SI/rlSew94BVcoqycnEgA3zBw9wV0zp7/GxDJjqNn+GaNjYVgfg10Ja3zyeVk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\n59EzrN1/yoh+TSVf7NNTim9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nmLJ0L8M6NGBIrBZmZyfhlcGteLpPc37cdJgHvlrP+dLW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x172ef139b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, label='Training acc')\n",
    "plt.plot(epochs, val_acc, label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, label='Training loss')\n",
    "plt.plot(epochs, val_loss, label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由於數據增加(data augmentation)和丟棄(dropout)的使用，我們不再有過度擬合(overfitting)的問題：訓練曲線相當密切地跟隨著驗證曲線。我們現在能夠達到82%的準確度，比非正規化的模型相比改善了15%。\n",
    "\n",
    "通過進一步利用正規化技術，及調整網絡參數（例如每個卷積層的濾波器數量或網絡層數），我們可以獲得更好的準確度，可能高達86 ~ 87%。然而，只要我們從頭開始訓練我們自己的卷積網絡(convnets)，我們可以證明使用這麼少的數據要來訓練出一個準確率高的模型是非常困難的。為了繼續提高我們模型對這個問題的準確性，下一步我們將利用預先訓練的模型(pre-trained model)來進行操作。"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "###  總結\n",
    "在這篇文章中有一些個人學習到的一些有趣的重點:\n",
    "* 善用數據擴充(Data Augmentation)對訓練資料不多的圖像辨識可以提升效能\n",
    "* Dropout的使用可以抑制過擬合(overfitting)的問題"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "參考: \n",
    "* [fchollet: deep-learning-with-python-notebooks](5.2-using-convnets-with-small-datasets.ipynb)\n",
    "* [Keras官網](http://keras.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MIT License\n",
    "\n",
    "Copyright (c) 2017 François Chollet\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "of this software and associated documentation files (the \"Software\"), to deal\n",
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    "copies of the Software, and to permit persons to whom the Software is\n",
    "furnished to do so, subject to the following conditions:\n",
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    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
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    "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "SOFTWARE."
   ]
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